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Medicaid managed care: how to target efforts to reduce costs
BACKGROUND: To be successful, cost control efforts must target Medicaid Managed Care (MMC) beneficiaries likely to incur high costs. The critical question is how to identify potential high cost beneficiaries with simple, reproducible, transparent, auditable criteria. Our objective in this analysis w...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4289361/ https://www.ncbi.nlm.nih.gov/pubmed/25395056 http://dx.doi.org/10.1186/1472-6963-14-461 |
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author | Charlson, Mary E Wells, Martin T Kanna, Balavenkatesh Dunn, Van Michelen, Walid |
author_facet | Charlson, Mary E Wells, Martin T Kanna, Balavenkatesh Dunn, Van Michelen, Walid |
author_sort | Charlson, Mary E |
collection | PubMed |
description | BACKGROUND: To be successful, cost control efforts must target Medicaid Managed Care (MMC) beneficiaries likely to incur high costs. The critical question is how to identify potential high cost beneficiaries with simple, reproducible, transparent, auditable criteria. Our objective in this analysis was to evaluate whether the total burden of comorbidity, assessed by the Charlson comorbidity index, could identify MMC beneficiaries who incurred high health care costs. METHODS: The MetroPlus MMC claims database was use to analyze six months of claims data from 07/07-12/07; the analysis focused on the total amount paid. Age, gender, Charlson comorbidity score, serious mental illness and pregnancy were analyzed as predictors of total costs. RESULTS: We evaluated the cost profile of 4,614 beneficiaries enrolled at MetroPlus, an MMC plan. As hypothesized, the comorbidity index was a key correlate of total costs (p < .01). Yearly costs were more related to the total burden of comorbidity than any specific comorbid disease. For adults, in addition to comorbidity (p < .01) both serious mental illness (p < .01) and pregnancy (p < .01) were also related to total costs, while age, drug addiction and gender were not. The model with age, gender, comorbidity, serious mental illness, pregnancy and addiction explained 20% of the variance in total costs. In children, comorbidity (p < .01), serious mental illness (p < .01), addiction (p < .03) and pregnancy (p < .01) were associated with log cost; the model with those variables explained 6% of the variance in costs. CONCLUSIONS: Comorbidity can be used to identify MMC beneficiaries most likely to have high costs. |
format | Online Article Text |
id | pubmed-4289361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42893612015-01-11 Medicaid managed care: how to target efforts to reduce costs Charlson, Mary E Wells, Martin T Kanna, Balavenkatesh Dunn, Van Michelen, Walid BMC Health Serv Res Research Article BACKGROUND: To be successful, cost control efforts must target Medicaid Managed Care (MMC) beneficiaries likely to incur high costs. The critical question is how to identify potential high cost beneficiaries with simple, reproducible, transparent, auditable criteria. Our objective in this analysis was to evaluate whether the total burden of comorbidity, assessed by the Charlson comorbidity index, could identify MMC beneficiaries who incurred high health care costs. METHODS: The MetroPlus MMC claims database was use to analyze six months of claims data from 07/07-12/07; the analysis focused on the total amount paid. Age, gender, Charlson comorbidity score, serious mental illness and pregnancy were analyzed as predictors of total costs. RESULTS: We evaluated the cost profile of 4,614 beneficiaries enrolled at MetroPlus, an MMC plan. As hypothesized, the comorbidity index was a key correlate of total costs (p < .01). Yearly costs were more related to the total burden of comorbidity than any specific comorbid disease. For adults, in addition to comorbidity (p < .01) both serious mental illness (p < .01) and pregnancy (p < .01) were also related to total costs, while age, drug addiction and gender were not. The model with age, gender, comorbidity, serious mental illness, pregnancy and addiction explained 20% of the variance in total costs. In children, comorbidity (p < .01), serious mental illness (p < .01), addiction (p < .03) and pregnancy (p < .01) were associated with log cost; the model with those variables explained 6% of the variance in costs. CONCLUSIONS: Comorbidity can be used to identify MMC beneficiaries most likely to have high costs. BioMed Central 2014-11-14 /pmc/articles/PMC4289361/ /pubmed/25395056 http://dx.doi.org/10.1186/1472-6963-14-461 Text en © Charlson et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Charlson, Mary E Wells, Martin T Kanna, Balavenkatesh Dunn, Van Michelen, Walid Medicaid managed care: how to target efforts to reduce costs |
title | Medicaid managed care: how to target efforts to reduce costs |
title_full | Medicaid managed care: how to target efforts to reduce costs |
title_fullStr | Medicaid managed care: how to target efforts to reduce costs |
title_full_unstemmed | Medicaid managed care: how to target efforts to reduce costs |
title_short | Medicaid managed care: how to target efforts to reduce costs |
title_sort | medicaid managed care: how to target efforts to reduce costs |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4289361/ https://www.ncbi.nlm.nih.gov/pubmed/25395056 http://dx.doi.org/10.1186/1472-6963-14-461 |
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